Bio-inspired metaheuristics for the generalised discrete cost multicommodity network design problem

Abstract

We investigate a new variant of network design problems (NDPs) called the generalised discrete cost multicommodity network design problem (GDCMNDP) that arises in a wide variety of real-life situations such as transportation, telecommunication and logistics. The problem consists on identifying the optimal capacitated network by choosing the connections to be installed in order to satisfy partially or totally the multicommodity demands. The objective is to minimise the sum of installation costs and penalty costs due to the unrouted demands. For the GDCMNDP, we propose three basic greedy heuristics and three bio-inspired metaheuristics: a basic genetic algorithm, a hybrid genetic algorithm via a variable neighbourhood search procedure and a biogeography-based optimisation heuristic. To assess the performance of the proposed approaches, computational results are reported using real-world and benchmark instances from the literature. Computational results show that our hybrid genetic algorithm performs well by obtaining very good final solutions in reasonable times.